The invention relates to the field of computing, and more specifically to composition of pattern-driven reactions in real-time dataflow programming, which may be used edge computing to handle the large amounts of data generated by industrial machines.
Traditional enterprise software application hosting has relied on datacenter or “cloud” infrastructure to exploit economies of scale and system efficiencies. However, these datacenters can be arbitrarily distant from the points of physical operations (e.g., factories, warehouses, retail stores, and others), where the enterprise conducts most of its business operations. The industrial Internet of things (IIoT) refers to a collection of devices or use-cases that relies on instrumentation of the physical operations with sensors that track events with very high frequency.
Industrial machines in many sectors com under this Internet of things (IoT) including manufacturing, oil and gas, mining, transportation, power and water, renewable energy, health care, retail, smart buildings, smart cities, and connected vehicles. Despite the success of cloud computing, there are number of shortcomings: It is not practical to send all of that data to cloud storage because connectivity may not always be there, bandwidth is not enough, variation in latencies is too high, or it is cost prohibitive even if bandwidth exists. Even if connectivity, bandwidth, and cost are not issues, there is no real-time decision making and predictive maintenance that can result in significant damage to the machines.
Therefore, improved computing systems, architectures, and techniques including improved edge analytics and dataflow programming are needed to handle the large amounts of data generated by industrial machines.